def test_places365(self):
for split, small in itertools.product(("train-standard", "train-challenge", "val"), (False, True)):
with places365_root(split=split, small=small) as places365:
root, data = places365
dataset = torchvision.datasets.Places365(root, split=split, small=small, download=True)
self.generic_classification_dataset_test(dataset, num_images=len(data["imgs"]))
def test_places365_devkit_no_download(self):
for split in ("train-standard", "train-challenge", "val"):
with self.subTest(split=split):
with places365_root(split=split) as places365:
root, data = places365
with self.assertRaises(RuntimeError):
torchvision.datasets.Places365(root, split=split, download=False)
def test_places365_images_download(self):
for split, small in itertools.product(("train-standard", "train-challenge", "val"), (False, True)):
with self.subTest(split=split, small=small):
with places365_root(split=split, small=small) as places365:
root, data = places365
dataset = torchvision.datasets.Places365(root, split=split, small=small, download=True)
assert all(os.path.exists(item[0]) for item in dataset.imgs)
def places365():
with log_download_attempts(patch=False) as urls_and_md5s:
for split, small in itertools.product(("train-standard", "train-challenge", "val"), (False, True)):
with places365_root(split=split, small=small) as places365:
root, data = places365
datasets.Places365(root, split=split, small=small, download=True)
return make_download_configs(urls_and_md5s, "Places365")
def test_places365_images_download_preexisting(self):
split = "train-standard"
small = False
images_dir = "data_large_standard"
with places365_root(split=split, small=small) as places365:
root, data = places365
os.mkdir(os.path.join(root, images_dir))
with self.assertRaises(RuntimeError):
torchvision.datasets.Places365(root, split=split, small=small, download=True)
def collect_urls_and_md5s(self):
with self.log_download_attempts(patch=False) as urls_and_md5s:
for split, small in itertools.product(
("train-standard", "train-challenge", "val"), (False, True)):
with places365_root(split=split, small=small) as places365:
root, data = places365
datasets.Places365(root,
split=split,
small=small,
download=True)
return urls_and_md5s
def places365():
with log_download_attempts(patch=False) as urls_and_md5s:
for split, small in itertools.product(
("train-standard", "train-challenge", "val"), (False, True)):
with places365_root(split=split, small=small) as places365:
root, data = places365
datasets.Places365(root,
split=split,
small=small,
download=True)
return [
DownloadConfig(url, md5=md5, id=f"Places365, {url}")
for url, md5 in urls_and_md5s
]
def test_places365_downloadable(self, download_url):
for split, small in itertools.product(
("train-standard", "train-challenge", "val"), (False, True)):
with places365_root(split=split, small=small) as places365:
root, data = places365
torchvision.datasets.Places365(root,
split=split,
small=small,
download=True)
urls = {call_args[0][0] for call_args in download_url.call_args_list}
for url in urls:
with self.subTest(url=url):
response = urlopen(Request(url, method="HEAD"))
assert response.code == 200, f"Server returned status code {response.code} for {url}."
def test_places365_devkit_download(self):
for split in ("train-standard", "train-challenge", "val"):
with self.subTest(split=split):
with places365_root(split=split) as places365:
root, data = places365
dataset = torchvision.datasets.Places365(root, split=split, download=True)
with self.subTest("classes"):
self.assertSequenceEqual(dataset.classes, data["classes"])
with self.subTest("class_to_idx"):
self.assertDictEqual(dataset.class_to_idx, data["class_to_idx"])
with self.subTest("imgs"):
self.assertSequenceEqual(dataset.imgs, data["imgs"])
def test_places365_transforms(self):
expected_image = "image"
expected_target = "target"
def transform(image):
return expected_image
def target_transform(target):
return expected_target
with places365_root() as places365:
root, data = places365
dataset = torchvision.datasets.Places365(
root, transform=transform, target_transform=target_transform, download=True
)
actual_image, actual_target = dataset[0]
self.assertEqual(actual_image, expected_image)
self.assertEqual(actual_target, expected_target)
def test_places365_repr_smoke(self):
with places365_root(extract_images=False) as places365:
root, data = places365
dataset = torchvision.datasets.Places365(root, download=True)
self.assertIsInstance(repr(dataset), str)